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fix: fix getChildren not correct after remove node (#13)
* fix: fix getChildren not correct after remove node * chore: update version and deps * chore: remove node 14 support
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,150 @@ | ||
| import { Graph } from '../../src/graph'; | ||
|
|
||
| describe('remove node and get children', () => { | ||
| const data = { | ||
| nodes: [ | ||
| { | ||
| id: 'Modeling Methods', | ||
| children: ['Classification', 'Consensus', 'Regression'], | ||
| }, | ||
| { | ||
| id: 'Classification', | ||
| children: [ | ||
| 'Logistic regression', | ||
| 'Linear discriminant analysis', | ||
| 'Rules', | ||
| 'Decision trees', | ||
| 'Naive Bayes', | ||
| 'K nearest neighbor', | ||
| 'Probabilistic neural network', | ||
| 'Support vector machine', | ||
| ], | ||
| }, | ||
| { id: 'Logistic regression' }, | ||
| { id: 'Linear discriminant analysis' }, | ||
| { id: 'Rules' }, | ||
| { id: 'Decision trees' }, | ||
| { id: 'Naive Bayes' }, | ||
| { id: 'K nearest neighbor' }, | ||
| { id: 'Probabilistic neural network' }, | ||
| { id: 'Support vector machine' }, | ||
| { id: 'Consensus', children: ['Models diversity', 'Methods', 'Common'] }, | ||
| { | ||
| id: 'Models diversity', | ||
| children: [ | ||
| 'Different initializations', | ||
| 'Different parameter choices', | ||
| 'Different architectures', | ||
| 'Different modeling methods', | ||
| 'Different training sets', | ||
| 'Different feature sets', | ||
| ], | ||
| }, | ||
| { id: 'Different initializations' }, | ||
| { id: 'Different parameter choices' }, | ||
| { id: 'Different architectures' }, | ||
| { id: 'Different modeling methods' }, | ||
| { id: 'Different training sets' }, | ||
| { id: 'Different feature sets' }, | ||
| { | ||
| id: 'Methods', | ||
| children: ['Classifier selection', 'Classifier fusion'], | ||
| }, | ||
| { id: 'Classifier selection' }, | ||
| { id: 'Classifier fusion' }, | ||
| { id: 'Common', children: ['Bagging', 'Boosting', 'AdaBoost'] }, | ||
| { id: 'Bagging' }, | ||
| { id: 'Boosting' }, | ||
| { id: 'AdaBoost' }, | ||
| { | ||
| id: 'Regression', | ||
| children: [ | ||
| 'Multiple linear regression', | ||
| 'Partial least squares', | ||
| 'Multi-layer feed forward neural network', | ||
| 'General regression neural network', | ||
| 'Support vector regression', | ||
| ], | ||
| }, | ||
| { id: 'Multiple linear regression' }, | ||
| { id: 'Partial least squares' }, | ||
| { id: 'Multi-layer feed forward neural network' }, | ||
| { id: 'General regression neural network' }, | ||
| { id: 'Support vector regression' }, | ||
| ], | ||
| edges: [ | ||
| { source: 'Modeling Methods', target: 'Classification' }, | ||
| { source: 'Modeling Methods', target: 'Consensus' }, | ||
| { source: 'Modeling Methods', target: 'Regression' }, | ||
| { source: 'Classification', target: 'Logistic regression' }, | ||
| { source: 'Classification', target: 'Linear discriminant analysis' }, | ||
| { source: 'Classification', target: 'Rules' }, | ||
| { source: 'Classification', target: 'Decision trees' }, | ||
| { source: 'Classification', target: 'Naive Bayes' }, | ||
| { source: 'Classification', target: 'K nearest neighbor' }, | ||
| { source: 'Classification', target: 'Probabilistic neural network' }, | ||
| { source: 'Classification', target: 'Support vector machine' }, | ||
| { source: 'Consensus', target: 'Models diversity' }, | ||
| { source: 'Consensus', target: 'Methods' }, | ||
| { source: 'Consensus', target: 'Common' }, | ||
| { source: 'Models diversity', target: 'Different initializations' }, | ||
| { source: 'Models diversity', target: 'Different parameter choices' }, | ||
| { source: 'Models diversity', target: 'Different architectures' }, | ||
| { source: 'Models diversity', target: 'Different modeling methods' }, | ||
| { source: 'Models diversity', target: 'Different training sets' }, | ||
| { source: 'Models diversity', target: 'Different feature sets' }, | ||
| { source: 'Methods', target: 'Classifier selection' }, | ||
| { source: 'Methods', target: 'Classifier fusion' }, | ||
| { source: 'Common', target: 'Bagging' }, | ||
| { source: 'Common', target: 'Boosting' }, | ||
| { source: 'Common', target: 'AdaBoost' }, | ||
| { source: 'Regression', target: 'Multiple linear regression' }, | ||
| { source: 'Regression', target: 'Partial least squares' }, | ||
| { | ||
| source: 'Regression', | ||
| target: 'Multi-layer feed forward neural network', | ||
| }, | ||
| { source: 'Regression', target: 'General regression neural network' }, | ||
| { source: 'Regression', target: 'Support vector regression' }, | ||
| ], | ||
| }; | ||
|
|
||
| const graph = new Graph({ | ||
| nodes: data.nodes.map(({ id }) => ({ id, data: {} })), | ||
| edges: data.edges.map(({ source, target }) => ({ | ||
| id: `${source}-${target}`, | ||
| source, | ||
| target, | ||
| data: {}, | ||
| })), | ||
| }); | ||
|
|
||
| graph.attachTreeStructure('tree'); | ||
|
|
||
| data.nodes.forEach((node) => { | ||
| node.children?.forEach((child) => { | ||
| graph.setParent(child, node.id, 'tree'); | ||
| }); | ||
| }); | ||
|
|
||
| it('should get the correct children', () => { | ||
| expect(graph.getParent('Logistic regression', 'tree')?.id).toBe( | ||
| 'Classification', | ||
| ); | ||
|
|
||
| graph.removeNode('Rules'); | ||
|
|
||
| expect( | ||
| graph.getChildren('Classification', 'tree').map((d) => d.id), | ||
| ).toEqual([ | ||
| 'Logistic regression', | ||
| 'Linear discriminant analysis', | ||
| // 'Rules', | ||
| 'Decision trees', | ||
| 'Naive Bayes', | ||
| 'K nearest neighbor', | ||
| 'Probabilistic neural network', | ||
| 'Support vector machine', | ||
| ]); | ||
| }); | ||
| }); |
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